Digital twin modeling

Tao, Fei; Xiao, Bin; Qi, Qinglin; Cheng, Jiangfeng; Ji, Ping

doi:10.1016/j.jmsy.2022.06.015

Cited by 499 publications

(147 citation statements)

References 95 publications

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“…Due to the stochastic nature of machine learning, the average error rate predicted by this set of data is the error rate of its model. As shown in Table 6, the average error rate trained by machine learning is 8.13%, which is much smaller than the 10% defined above and even smaller than the error rate predicted by the empirical (9). Therefore, it can be shown that the data prediction by machine learning has correctness and is equally applicable in the case of small samples and a small range of values of experimental parameters.…”

Section: Association Between Physical Entities and Virtual Modelsmentioning

confidence: 77%

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Digital Twin Application in Ground Simulating Space Debris System With Laser-Driven Flyer Technology

et al. 2022

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In recent years, with the continuous development of spacecraft, the demand for experimental and analytical simulation of space debris impact is increasing. Therefore, the simulation of space debris technology such as laser-driven flyer (LDF) is becoming increasingly strategic. However, in traditional LDF experiments, the experimental environment is difficult to control and the number of experiments is constrained by cost, resulting in only a small amount of valid data available. This makes it difficult to evaluate the sample damage. In this paper, thus, a method of LDF digital twinning with two phases and its experimental equipment is proposed. During the experimental phase, it provides a standardized experimental environment, while its experimental equipment facilitates automated, remote experiments and virtual training. In the evaluation phase, it is possible to use neural networks to enrich a small amount of experimental data into high-throughput data containing physical damage parameters, and finite element simulation to obtain microscopic damage processes and related data. Both methods have been validated by additional experiments, and the average error rate is 8.13% for the neural network and 3.1% for the simulation, using the fragmentation zone diameter as the predicted object. Again, both of these are much smaller than the 10% error rate, which is defined by the analysis of the experimental data. In addition, this paper proposes a fullcycle experimental path that is more compact, saves experimental costs, and reduces experimental cycle time compared with the traditional experimental path.

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Section: Association Between Physical Entities and Virtual Modelsmentioning

confidence: 77%

“…In recent years, digital twin (DT) technology has become a popular method for building up real and virtual spaces [9]. It can automate, remote, and visualize the control operation of traditional equipment [10] as well as virtual training of operation [11].…”

Section: Introductionmentioning

confidence: 99%

Digital Twin Application in Ground Simulating Space Debris System With Laser-Driven Flyer Technology

et al. 2022

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“…Digital twins aim to build a “complete and independent mirror” of the physical world in the digital world through digital means. The mirror model can maintain the real-time interactive connection with physical entities and realize the understanding, analysis, and optimization of physical entities through simulation, verification, prediction, and control of the whole life cycle process of physical entities through historical data, real-time data, and the algorithm model [ 17 ]. In simpler terms, digital twins clone a device or system as a digital version [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

Application of Digital Twins and Metaverse in the Field of Fluid Machinery Pumps and Fans: A Review

Yang

et al. 2022

Sensors

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Digital twins technology (DTT) is an application framework with breakthrough rules. With the deep integration of the virtual information world and physical space, it becomes the basis for realizing intelligent machining production lines, which is of great significance to intelligent processing in industrial manufacturing. This review aims to study the application of DTT and the Metaverse in fluid machinery in the past 5 years by summarizing the application status of pumps and fans in fluid machinery from the perspective of DTT and the Metaverse through the collection, classification, and summary of relevant literature in the past 5 years. The research found that in addition to relatively mature applications in intelligent manufacturing, DTT and Metaverse technologies play a critical role in the development of new pump products and technologies and are widely used in numerical simulation and fault detection in fluid machinery for various pumps and other fields. Among fan-type fluid machinery, twin fans can comprehensively use technologies, such as perception, calculation, modeling, and deep learning, to provide efficient smart solutions for fan operation detection, power generation visualization, production monitoring, and operation monitoring. Still, there are some limitations. For example, real-time and accuracy cannot fully meet the requirements in the mechanical environment with high-precision requirements. However, there are also some solutions that have achieved good results. For instance, it is possible to achieve significant noise reduction and better aerodynamic performance of the axial fan by improving the sawtooth parameters of the fan and rearranging the sawtooth area. However, there are few application cases of the Metaverse in fluid machinery. The cases are limited to operating real equipment from a virtual environment and require the combination of virtual reality and DTT. The application effect still needs further verification.

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“…The simultaneous flow of information in DT allow to optimize the technology for better performance. This bidirectional dynamic relationship between physical and virtual models can improve the efficiency of product design, manufacturing/machining process, and service throughout the system's life cycle [33][34][35]. Digital twins successfully employed in manufacturing processes including: quality management [36,37] to determine the quality problem and selection of better material and process, logistic planning [38] to optimize the supply chain, product development [39] by incorporating user experience, product redesign [40] by checking the compatibility of existing equipment against new design by simulating in a DT.…”

Section: Digital Twinmentioning

confidence: 99%

Modeling, Analysis, and Optimization of Robotic Light Machining Tasks for Empowering Digital Twin: Generalized Impulse Model Approach

et al. 2022

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Light machining tasks by robots are becoming an important issue to replace shortages of human resources. To improve the quality, safety and overall performance of manufacturing process, the modeling for estimation of forces and torques during the machining operations is on demand. In parallel, the digital model has also been developed which allow to detect the foul conditions, save energy & time and optimization of the real manufacturing process. Digital twins are one of them which use the offline and online data to simulate the physical manufacturing process. However, the empowerment of digital twins can be improved further by developing more accurate mathematical model which allow to simulate the physical machining process in real time. Accordingly, this paper presents a formulation for the mechanics of robotic light machining tasks to empower the digital twin. In this paper, a generalized impulse model is employed to analyze a light machining task that combines the linear and angular motions. For the implementation of an impulse model-based approach, the concept of both effective mass and effective inertia is newly introduced to reflect the dynamics of the environment, which depends on the hardness of the material and process parameters (feed rate and speed (rpm) etc.) of the machining task. Furthermore, optimal feed rates are calculated with consideration of effective mass/effective inertia and minimum task completion time. Moreover, simulations are carried out to choose the feasible direction of linear and angular velocities and optimal non-singular workspace for light machining tasks. Finally, the proposed methodology is validated through a quantitative comparison of simulation and experimental results by performing the drilling and milling tasks. A 6-DOF Universal robot (UR 5e) is used for simulations and experiments to corroborate the effectiveness of the proposed algorithms for light machining tasks. The developed methodology will certainly empower the digital twin for their physical analog during light machining operations.

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Digital twin modeling

Cited by 499 publications

References 95 publications

Digital Twin Application in Ground Simulating Space Debris System With Laser-Driven Flyer Technology

Digital Twin Application in Ground Simulating Space Debris System With Laser-Driven Flyer Technology

Application of Digital Twins and Metaverse in the Field of Fluid Machinery Pumps and Fans: A Review

Modeling, Analysis, and Optimization of Robotic Light Machining Tasks for Empowering Digital Twin: Generalized Impulse Model Approach

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